This report presents the ranking model developed for evaluating and prioritizing online job advertisement (OJA) sources within the Web Intelligence Hub (WIH). The model serves as a decision-support tool to identify the most relevant sources for content retrieval, ensuring high-quality OJA data. It integrates both quantitative and qualitative assessments to rank sources based on their desirability and relevance.
The ranking methodology consists of two core components: (1) the Analytic Hierarchy Process (AHP) score, which quantitatively measures the alignment of a source’s characteristics with predefined quality criteria, and (2) the International Country Experts (ICEs) ranking, which incorporates expert assessments of source popularity, stability, and coverage within national OJA markets. These components are combined into a final score that determines the priority level of each source for inclusion in the Data Production System (DPS).
The report outlines the decision rules for ranking, considering both new landscaping exercises and update of existing landscaping exercises. A structured selection process ensures that only high-priority sources are selected for web crawling development. The final ranking results inform subsequent content retrieval and processing strategies, supporting the establishment of a robust and scalable system for OJA data.
This ranking model contributes to enhancing the quality of online job advertisements data production, ultimately improving labor market statistics and analysis.
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